The Journey to Improve LCPM: An R Package for Ordinal Regression
Volume 23, Issue 2 (2025): Special Issue: the 2024 Symposium on Data Science and Statistics (SDSS), pp. 399–415
Pub. online: 5 May 2025
Type: Computing In Data Science
Open Access
Open Access
Received
3 October 2024
3 October 2024
Accepted
4 April 2025
4 April 2025
Published
5 May 2025
5 May 2025
Abstract
Recently, the log cumulative probability model (LCPM) and its special case the proportional probability model (PPM) was developed to relate ordinal outcomes to predictor variables using the log link instead of the logit link. These models permit the estimation of probability instead of odds, but the log link requires constrained maximum likelihood estimation (cMLE). An algorithm that efficiently handles cMLE for the LCPM is a valuable resource as these models are applicable in many settings and its output is easy to interpret. One such implementation is in the R package lcpm. In this era of big data, all statistical models are under pressure to meet the new processing demands. This work aimed to improve the algorithm in R package lcpm to process more input in less time using less memory.
Supplementary material
Supplementary MaterialThe supplementary zip file contains all source code (both R and CPP), an R package used to run test case D, a sample windows batch script to run the code and 3 sample csv files that are input into the batch file. There is also a readme file included with more explanation on how to use these files.
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